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Theoretical and numerical comparison of the Karush–Kuhn–Tucker and value function reformulations in bilevel optimization
Theoretical and numerical comparison of the Karush–Kuhn–Tucker and value function reformulations in bilevel optimization
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Theoretical and numerical comparison of the Karush–Kuhn–Tucker and value function reformulations in bilevel optimization
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Theoretical and numerical comparison of the Karush–Kuhn–Tucker and value function reformulations in bilevel optimization
Theoretical and numerical comparison of the Karush–Kuhn–Tucker and value function reformulations in bilevel optimization

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Theoretical and numerical comparison of the Karush–Kuhn–Tucker and value function reformulations in bilevel optimization
Theoretical and numerical comparison of the Karush–Kuhn–Tucker and value function reformulations in bilevel optimization
Journal Article

Theoretical and numerical comparison of the Karush–Kuhn–Tucker and value function reformulations in bilevel optimization

2021
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Overview
The Karush–Kuhn–Tucker and value function (lower-level value function, to be precise) reformulations are the most common single-level transformations of the bilevel optimization problem. So far, these reformulations have either been studied independently or as a joint optimization problem in an attempt to take advantage of the best properties from each model. To the best of our knowledge, these reformulations have not yet been compared in the existing literature. This paper is a first attempt towards establishing whether one of these reformulations is best at solving a given class of the optimistic bilevel optimization problem. We design a comparison framework, which seems fair, considering the theoretical properties of these reformulations. This work reveals that although none of the models seems to particularly dominate the other from the theoretical point of view, the value function reformulation seems to numerically outperform the Karush–Kuhn–Tucker reformulation on a Newton-type algorithm. The computational experiments here are mostly based on test problems from the Bilevel Optimization LIBrary (BOLIB).
Publisher
Springer Nature B.V